class_docstr: Hellinger Distance split criterion.
  
  The Hellinger distance is a measure of distributional divergence.
  It is used as the splitting criterion [^1] on decision trees to to address
  the imbalanced data problem.
  
  References
  ----------
  [^1]: Cieslak, David A., T. Ryan Hoens, Nitesh V. Chawla, and W. Philip Kegelmeyer.
  "Hellinger distance decision trees are robust and skew-insensitive." Data Mining
  and Knowledge Discovery 24, no. 1 (2012): 136-158.
code: def merit_of_split(self, pre_split_dist, post_split_dist):
      if (
          self.num_subsets_greater_than_frac(
              post_split_dist, self.min_branch_fraction
          )
          < 2
      ):
          return -math.inf
      return self.compute_hellinger(post_split_dist)
  
file_path: river/river/tree/split_criterion/hellinger_distance_criterion.py
imports: from __future__ import annotations
  import math
  from .base import SplitCriterion
label_desc: Use image node_id 2 for calling the HellingerDistanceCriterion obj's underlying member method code with example usage: obj.merit_of_split(pre_split_dist, post_split_dist) and returns: self, unknown